SHADOWCAST: CONTROLLABLE GRAPH GENERA-TION WITH EXPLAINABILITY

Abstract

We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce desired graphs with explainable structures. By directing this generative process, we can explain the observed outcomes. We propose SHADOWCAST, a controllable generative model capable of mimicking networks and directing the generation, as an approach to this novel problem. The proposed model is based on a conditional generative adversarial network for graph data. We design it with the capability to control the conditions using a simple and transparent Markov model. Comprehensive experiments on three real-world network datasets demonstrate our model's competitive performance in the graph generation task. Furthermore, we control SHADOWCAST to generate graphs of different structures to show its effective controllability and explainability. As the first work to pose the problem of explaining generated graphs by controlling the generation, SHADOWCAST paves the way for future research in this exciting area.

1. INTRODUCTION

In many real-world networks, including but not limited to communication, financial, and social networks, graph generative models are applied to model relationships among actors. It is crucial that the models not only mimic the structure of observed networks but also generate graphs with desired properties because it allows for an increased understanding of these relationships. Currently, there are no such methods for explaining graph generation. Meaningful interactions between agents are often investigated under different what-if scenarios, which determines the feasibility of the interactions under abnormal and unforeseen circumstances. In such investigations, instead of using actual data, we can generate synthetic data to study and test the systems (Barse et al., 2003; Skopik et al., 2014) . However, there are many challenges. (1) Data is not accessible by direct measurement of the system. (2) Data is not available. (3) Data produced by generative models cannot be explained. To address these challenges, we have to answer a natural and meaningful question: Can we control the generative process to shape and explain the generated graphs? In this work, we introduce the novel problem of explaining graph generation. The goal is to generate graphs of desired shapes by learning to control the associated graph properties and structure to influence the generative process. We provide an illustrative case study of email communications in an organization with two departments (Figure 1 ), where interactions of the employees follow a regular pattern during normal operations. Due to limited data, previously observed network information may be missing scenarios of intra-department email surge within either the Human Resources or Accounting departments. When such situations are required for analyzing the system, an ideal model should generate graphs that reflect these scenarios (see box in Figure 1 ) while maintaining the organizational structure. By effectively controlling the generative process, SHADOWCAST allows users to generate designed graphs that meet conditions resembling a wide range of possibilities. Overall, this is a meaningful problem because controlling the generative process to explain generated networks proves to be valuable in many applications such as anomaly detection and data augmentation. Existing graph generative models aim to mimic the structure of given networks, but they cannot easily shape graphs into other desired states. These works either directly capture the graph structure (Cao & Kipf, 2018; Liu et al., 2017; Tavakoli et al., 2017; Zhou et al., 2019; Ma et al., 2018; You et al., 2018; Simonovsky & Komodakis, 2018; Bojchevski et al., 2018) or model node feature

